Computational Intelligence for Bioinformatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 469

Special Issue Editors


E-Mail Website
Guest Editor
College of Computer Science and Technology, Qingdao University, Qingdao, China
Interests: microbiome; metagenome; machine learning; algorithm

E-Mail Website
Guest Editor
School of Software, Xinjiang University, Urumqi, China
Interests: medical image processing; bioinformatics; algorithm

Special Issue Information

Dear Colleagues,

Rapid advancements in computational intelligence are transforming the field of bioinformatics, enabling researchers to analyze complex biological data with unprecedented accuracy and efficiency. This Mathematics Special Issue is dedicated to exploring novel computational approaches—including machine learning, deep learning, evolutionary algorithms and mathematical modeling—that drive innovation in bioinformatics. We welcome contributions that address key challenges in genomics, proteomics, systems biology and medical informatics, with a focus on algorithm development, data integration and predictive analytics. By bringing together cutting-edge research, this issue aims to highlight the synergy between computational intelligence and life sciences, fostering new insights and technological breakthroughs in biomedical research.

Dr. Xiaoquan Su
Dr. Min Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational intelligence
  • bioinformatics
  • machine learning
  • mathematical modeling
  • biomedical data analysis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 4524 KiB  
Article
MT-Tracker: A Phylogeny-Aware Algorithm for Quantifying Microbiome Transitions Across Scales and Habitats
by Wenjie Zhu, Yangyang Sun, Weiwen Luo, Guosen Hou, Hao Gao and Xiaoquan Su
Mathematics 2025, 13(12), 1982; https://doi.org/10.3390/math13121982 - 16 Jun 2025
Viewed by 330
Abstract
The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address [...] Read more.
The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address this gap, we introduce MT-tracker (microbiome transition tracker), a novel algorithm designed to capture the transitional trajectories of microbial communities. Grounded in diversity and phylogenetic principles, MT-tracker reconstructs the virtual common ancestors of microbiomes at the community level. By calculating distances between microbiomes and their ancestors, MT-tracker deduces their transitional directions and probabilities, achieving a substantial speed advantage over conventional approaches. The accuracy and robustness of MT-tracker were first validated by a phylosymbiosis analysis using samples from 28 mammals and 24 nonmammal animals, describing the co-evolutionary pattern between hosts and their associated microbiomes. We then expanded the usage of MT-tracker to 456,702 microbiomes sampled world-wide, uncovering the global transitional directions among 21 ecosystems for the first time. This effort provides new insights into the macro-scale dynamic patterns of microbial communities. Additionally, MT-tracker revealed intricate longitudinal transition trends in human microbiomes over a sampling period exceeding 400 days, capturing temporal dynamics often overlooked by normal diversity analyses. In summary, MT-tracker offers robust support for the qualitative and quantitative analysis of microbial community diversity, offering significant potential for studying and utilizing the macrobiome variation. Full article
(This article belongs to the Special Issue Computational Intelligence for Bioinformatics)
Show Figures

Figure 1

Back to TopTop